iTrust: Encounter Based Trust recommendation System

Udayan Kumar and Dr. Ahmed Helmy

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Encounter based Trust: iTrust

• Questions/Motivation

– Although so powerful yet these devices do not fully utilize peer to peer interactions. Why?

– What can we do if we start leveraging the power of P2P communication in mobile networks? Applications are plenty.

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Need for Trust

• To break psychological barriers in using AdHoc and Peer-to-Peer mobile services. (I don’t know anything about this device/user. Should I communicate?).

• Will people communicate without knowledge of other devices?

• Bootstrap recommendation, reputation or credit based system. (I believe in yellow credit and you believe in green. I think green is fraudulent, why should I trust you).

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Promise of iTrust

• To be a unified communication oriented trust recommendation framework for mobile devices

• To capture socially relevant trust information using social science principle of homophiliy[Mcp01]

• Allow proximity based interactions unavailable in wired networks. Out-of-band communication (can be secured using key exchanges [Che08])

• Encourage interactions in mobile societies and adoption of new mobile services.

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Illustration: iTrust

I recommend Device B due to

high trust score. It has encountered

you at ….

AB

Hey B. Can we Hang out?Hey A. Yes, why

not!

Out-of-band Key Exchange

Lets exchange keys

iTrust helped A and B discover each other. They may turn out be lifelong friends .

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Definitions in Literature 1. makes cooperative endeavors happen (e.g., Arrow, 1974; Deutsch, 1973;

Gambetta, 1988)2. cooperating or task coordinating (e.g., Solomon, 1960);3. placing resources or authority in the other party's hands (Coleman, 1990;

Shapiro, 1987a);4. being influenced by the other (e.g., Bonoma, 1976);5. committing to a possible loss based on the other's actions (Anderson & Narus,

1990);6. placing resources or authority in the other party's hands (Coleman, 1990;

Shapiro, 1987a);7. providing open/honest information (e.g., Mishra, 1993);8. entering informal agreements (Currall & Judge, 1995);9. increasing one's vulnerability (e.g., Zand, 1972);10.reducing one's control over the other (Dobing, 1993);11.risk taking (e.g., Coleman, 1990; Mayer, Davis & Schoorman, 1995);12.increasing the scope of the other person's discretionary power (Baier, 1986);13.reducing the rules we place on the other's behavior (Fox, 1974)14.involving subordinates in decision making (Carnevale & Wechsler, 1992).

TrustActual

human trust

ContextSocial

Interactions

Online social networks

Real World interactions

Face to Face Interactions

Similarity (Homophiliy) --

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Goals

• Stability – In trust recommendations• Distributed Operation - In calculations• Privacy-Preservation – minimize the need of

data exchange.• Accuracy – when measuring similarity• Resilience – from anomalies such as

artificially induced encounters.• Energy Efficiency

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Architecture Overview

Trust Scores

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Trust Adviser Filters• Frequency of Encounter (FE) -- Encounter count • Duration of Encounter (DE) – Encounter duration Proposed:• Profile Vector – Location based similarity using vectors.• Location Vector – Location based similarity using

vectors – Count and Duration (Privacy preserving)• Behavior Matrix – Location based similarity (using

matrix) – Count and Duration [HSU08]

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Filters

B’s Profile Vector

A’s Profile Vector

Profile Vector Exchange for similarity calculationsB A

B

Profile Vector (PV):

Location Vector (LV) :

Maintains a vector for itself

Maintains a vector for itself

Creates and manages vector for every user encountered

Vector for other users are populated with only the information B has witnessed

No exchange of vectors is needed !! Privacy preserving

Each cell represents a location(such as dorm, ofc)

Each cell stores count/duration at that location

Vector

4 32

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4 32

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Day 1Day 2

Day N

Behavior Matrix (BM):

B’s Matrix Summary

A’s Matrix Summary

Behavior Matrix Exchange for similarity calculationsB A

Maintains a Matrix for itself

This matrix is summarized using SVD. The summary is exchanged b/w the users to calculate similariy

Each cell stores count/duration at that location

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Combined Filter (H)

• In Combined filter we combine trust scores from all the filters to provide a unified trust score.

H (Uj) = Σ αiFi(Uj), where αi is the weight for

Filter Fi, n is the total number of filters

• Different people may prefer different weights (observed from the user feedback on implementation). Eventually it can be made adaptive.

n